Overview

Dataset statistics

Number of variables12
Number of observations1318
Missing cells1382
Missing cells (%)8.7%
Duplicate rows639
Duplicate rows (%)48.5%
Total size in memory123.7 KiB
Average record size in memory96.1 B

Variable types

Numeric10
Categorical2

Alerts

Dataset has 639 (48.5%) duplicate rowsDuplicates
p is highly overall correlated with cc and 1 other fieldsHigh correlation
t is highly overall correlated with sHigh correlation
ts is highly overall correlated with tdHigh correlation
td is highly overall correlated with tsHigh correlation
cc is highly overall correlated with pHigh correlation
pp is highly overall correlated with pHigh correlation
s is highly overall correlated with tHigh correlation
u has 46 (3.5%) missing valuesMissing
gs has 18 (1.4%) missing valuesMissing
c has 18 (1.4%) missing valuesMissing
hdl has 25 (1.9%) missing valuesMissing
cc has 189 (14.3%) missing valuesMissing
pp has 1064 (80.7%) missing valuesMissing

Reproduction

Analysis started2023-11-20 17:53:40.978486
Analysis finished2023-11-20 17:53:46.387716
Duration5.41 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

e
Real number (ℝ)

Distinct61
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.580425
Minimum18
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:46.439852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q131
median42
Q351
95-th percentile61
Maximum88
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.564518
Coefficient of variation (CV)0.30217386
Kurtosis-0.29988075
Mean41.580425
Median Absolute Deviation (MAD)9
Skewness0.25175625
Sum54803
Variance157.8671
MonotonicityNot monotonic
2023-11-20T11:53:46.495292image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 53
 
4.0%
47 49
 
3.7%
43 49
 
3.7%
52 48
 
3.6%
38 46
 
3.5%
24 44
 
3.3%
51 42
 
3.2%
33 40
 
3.0%
46 39
 
3.0%
41 38
 
2.9%
Other values (51) 870
66.0%
ValueCountFrequency (%)
18 4
 
0.3%
19 6
 
0.5%
20 10
 
0.8%
21 16
 
1.2%
22 35
2.7%
23 20
1.5%
24 44
3.3%
25 32
2.4%
26 38
2.9%
27 30
2.3%
ValueCountFrequency (%)
88 1
 
0.1%
85 2
 
0.2%
83 3
0.2%
82 1
 
0.1%
80 1
 
0.1%
77 2
 
0.2%
75 1
 
0.1%
74 1
 
0.1%
71 2
 
0.2%
70 7
0.5%

s
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
1
907 
2
411 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1318
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

Length

2023-11-20T11:53:46.546012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T11:53:46.595455image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

Most occurring characters

ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1318
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

Most occurring scripts

ValueCountFrequency (%)
Common 1318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 907
68.8%
2 411
31.2%

u
Categorical

Distinct4
Distinct (%)0.3%
Missing46
Missing (%)3.5%
Memory size10.4 KiB
1.0
804 
4.0
234 
3.0
118 
2.0
116 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3816
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 804
61.0%
4.0 234
 
17.8%
3.0 118
 
9.0%
2.0 116
 
8.8%
(Missing) 46
 
3.5%

Length

2023-11-20T11:53:46.632647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-20T11:53:46.679048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 804
63.2%
4.0 234
 
18.4%
3.0 118
 
9.3%
2.0 116
 
9.1%

Most occurring characters

ValueCountFrequency (%)
. 1272
33.3%
0 1272
33.3%
1 804
21.1%
4 234
 
6.1%
3 118
 
3.1%
2 116
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2544
66.7%
Other Punctuation 1272
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1272
50.0%
1 804
31.6%
4 234
 
9.2%
3 118
 
4.6%
2 116
 
4.6%
Other Punctuation
ValueCountFrequency (%)
. 1272
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3816
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1272
33.3%
0 1272
33.3%
1 804
21.1%
4 234
 
6.1%
3 118
 
3.1%
2 116
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3816
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1272
33.3%
0 1272
33.3%
1 804
21.1%
4 234
 
6.1%
3 118
 
3.1%
2 116
 
3.0%

p
Real number (ℝ)

Distinct318
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.261129
Minimum38
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:46.728837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile51.97
Q161.025
median68.75
Q378
95-th percentile96.45
Maximum129
Range91
Interquartile range (IQR)16.975

Descriptive statistics

Standard deviation13.434725
Coefficient of variation (CV)0.19121135
Kurtosis0.70404764
Mean70.261129
Median Absolute Deviation (MAD)8.75
Skewness0.72251276
Sum92604.168
Variance180.49184
MonotonicityNot monotonic
2023-11-20T11:53:46.782110image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 28
 
2.1%
63 28
 
2.1%
73 26
 
2.0%
74 24
 
1.8%
68 23
 
1.7%
64 23
 
1.7%
69 22
 
1.7%
58 21
 
1.6%
60 20
 
1.5%
71 20
 
1.5%
Other values (308) 1083
82.2%
ValueCountFrequency (%)
38 1
 
0.1%
40.5 2
0.2%
41 2
0.2%
42 4
0.3%
43 2
0.2%
45 1
 
0.1%
45.2 2
0.2%
46.2 1
 
0.1%
46.7 2
0.2%
47.7 2
0.2%
ValueCountFrequency (%)
129 1
 
0.1%
124.5 2
0.2%
112.3 2
0.2%
110 3
0.2%
109.9 2
0.2%
109 2
0.2%
108 3
0.2%
107.8 2
0.2%
106.75 4
0.3%
105 2
0.2%

t
Real number (ℝ)

Distinct48
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5995448
Minimum1.34
Maximum1.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:46.833837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1.34
5-th percentile1.47
Q11.54
median1.59
Q31.66
95-th percentile1.75
Maximum1.91
Range0.57
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.087252533
Coefficient of variation (CV)0.054548353
Kurtosis0.10952783
Mean1.5995448
Median Absolute Deviation (MAD)0.06
Skewness0.45882023
Sum2108.2
Variance0.0076130044
MonotonicityNot monotonic
2023-11-20T11:53:46.884263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1.54 68
 
5.2%
1.6 68
 
5.2%
1.55 66
 
5.0%
1.56 64
 
4.9%
1.57 62
 
4.7%
1.52 61
 
4.6%
1.59 60
 
4.6%
1.5 52
 
3.9%
1.62 51
 
3.9%
1.58 50
 
3.8%
Other values (38) 716
54.3%
ValueCountFrequency (%)
1.34 1
 
0.1%
1.38 2
 
0.2%
1.39 1
 
0.1%
1.41 1
 
0.1%
1.42 5
 
0.4%
1.43 6
 
0.5%
1.44 12
0.9%
1.45 17
1.3%
1.47 25
1.9%
1.48 24
1.8%
ValueCountFrequency (%)
1.91 2
 
0.2%
1.89 4
 
0.3%
1.85 2
 
0.2%
1.83 6
0.5%
1.82 10
0.8%
1.81 2
 
0.2%
1.8 2
 
0.2%
1.79 8
0.6%
1.78 10
0.8%
1.77 6
0.5%

ts
Real number (ℝ)

Distinct82
Distinct (%)6.3%
Missing11
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean118.86075
Minimum12
Maximum185
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:46.937863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile96
Q1108
median118
Q3128
95-th percentile148
Maximum185
Range173
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.282504
Coefficient of variation (CV)0.13698807
Kurtosis1.6719846
Mean118.86075
Median Absolute Deviation (MAD)10
Skewness0.35623591
Sum155351
Variance265.11995
MonotonicityNot monotonic
2023-11-20T11:53:46.988230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120 73
 
5.5%
100 58
 
4.4%
110 53
 
4.0%
121 44
 
3.3%
117 40
 
3.0%
123 38
 
2.9%
124 34
 
2.6%
108 32
 
2.4%
105 32
 
2.4%
111 32
 
2.4%
Other values (72) 871
66.1%
ValueCountFrequency (%)
12 1
 
0.1%
80 2
 
0.2%
84 2
 
0.2%
85 2
 
0.2%
86 2
 
0.2%
87 6
0.5%
88 2
 
0.2%
89 2
 
0.2%
90 12
0.9%
91 2
 
0.2%
ValueCountFrequency (%)
185 2
 
0.2%
170 3
 
0.2%
165 4
0.3%
164 2
 
0.2%
163 4
0.3%
162 4
0.3%
160 9
0.7%
159 2
 
0.2%
158 2
 
0.2%
155 2
 
0.2%

td
Real number (ℝ)

Distinct49
Distinct (%)3.7%
Missing11
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean75.240245
Minimum50
Maximum115
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.044385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile60
Q170
median75
Q381
95-th percentile89
Maximum115
Range65
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.9138536
Coefficient of variation (CV)0.11847188
Kurtosis0.3348497
Mean75.240245
Median Absolute Deviation (MAD)6
Skewness0.022413703
Sum98339
Variance79.456786
MonotonicityNot monotonic
2023-11-20T11:53:47.095006image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
70 108
 
8.2%
80 84
 
6.4%
75 59
 
4.5%
81 56
 
4.2%
77 56
 
4.2%
60 54
 
4.1%
73 54
 
4.1%
72 52
 
3.9%
74 47
 
3.6%
78 45
 
3.4%
Other values (39) 692
52.5%
ValueCountFrequency (%)
50 5
 
0.4%
51 4
 
0.3%
52 2
 
0.2%
53 2
 
0.2%
54 2
 
0.2%
56 5
 
0.4%
57 4
 
0.3%
58 4
 
0.3%
59 6
 
0.5%
60 54
4.1%
ValueCountFrequency (%)
115 2
 
0.2%
103 2
 
0.2%
99 4
 
0.3%
98 2
 
0.2%
96 6
 
0.5%
94 6
 
0.5%
93 2
 
0.2%
92 4
 
0.3%
91 8
 
0.6%
90 27
2.0%

gs
Real number (ℝ)

Distinct86
Distinct (%)6.6%
Missing18
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean96.794615
Minimum64
Maximum378
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.149439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum64
5-th percentile76
Q184
median90
Q398
95-th percentile138.05
Maximum378
Range314
Interquartile range (IQR)14

Descriptive statistics

Standard deviation30.500144
Coefficient of variation (CV)0.31510166
Kurtosis27.850324
Mean96.794615
Median Absolute Deviation (MAD)7
Skewness4.6925606
Sum125833
Variance930.25879
MonotonicityNot monotonic
2023-11-20T11:53:47.199626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 67
 
5.1%
86 55
 
4.2%
88 54
 
4.1%
93 54
 
4.1%
87 52
 
3.9%
95 51
 
3.9%
89 47
 
3.6%
83 44
 
3.3%
80 43
 
3.3%
90 42
 
3.2%
Other values (76) 791
60.0%
ValueCountFrequency (%)
64 2
 
0.2%
66 4
 
0.3%
71 8
 
0.6%
72 12
0.9%
73 16
1.2%
74 8
 
0.6%
75 12
0.9%
76 29
2.2%
77 20
1.5%
78 18
1.4%
ValueCountFrequency (%)
378 2
0.2%
317 2
0.2%
303 4
0.3%
274 1
 
0.1%
262 2
0.2%
256 2
0.2%
255 4
0.3%
243 2
0.2%
236 2
0.2%
216 2
0.2%

c
Real number (ℝ)

Distinct151
Distinct (%)11.6%
Missing18
Missing (%)1.4%
Infinite0
Infinite (%)0.0%
Mean186.34077
Minimum80
Maximum356
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.385556image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile132.9
Q1159
median182
Q3211
95-th percentile247
Maximum356
Range276
Interquartile range (IQR)52

Descriptive statistics

Standard deviation38.346984
Coefficient of variation (CV)0.20578955
Kurtosis1.0685001
Mean186.34077
Median Absolute Deviation (MAD)25
Skewness0.57554491
Sum242243
Variance1470.4912
MonotonicityNot monotonic
2023-11-20T11:53:47.437062image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175 27
 
2.0%
204 24
 
1.8%
173 22
 
1.7%
161 22
 
1.7%
176 22
 
1.7%
167 22
 
1.7%
172 20
 
1.5%
242 20
 
1.5%
195 20
 
1.5%
158 20
 
1.5%
Other values (141) 1081
82.0%
ValueCountFrequency (%)
80 1
 
0.1%
85 2
 
0.2%
94 4
0.3%
97 2
 
0.2%
99 6
0.5%
108 2
 
0.2%
109 2
 
0.2%
110 2
 
0.2%
115 2
 
0.2%
117 4
0.3%
ValueCountFrequency (%)
356 2
0.2%
347 2
0.2%
341 2
0.2%
316 2
0.2%
302 2
0.2%
301 2
0.2%
295 2
0.2%
290 2
0.2%
282 2
0.2%
281 2
0.2%

hdl
Real number (ℝ)

Distinct59
Distinct (%)4.6%
Missing25
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean44.132251
Minimum21
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.493108image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile29
Q136
median43
Q350
95-th percentile63
Maximum238
Range217
Interquartile range (IQR)14

Descriptive statistics

Standard deviation12.810369
Coefficient of variation (CV)0.29027229
Kurtosis63.139198
Mean44.132251
Median Absolute Deviation (MAD)7
Skewness4.9573555
Sum57063
Variance164.10556
MonotonicityNot monotonic
2023-11-20T11:53:47.542851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 70
 
5.3%
42 64
 
4.9%
40 62
 
4.7%
39 58
 
4.4%
46 56
 
4.2%
45 53
 
4.0%
41 47
 
3.6%
33 44
 
3.3%
34 42
 
3.2%
50 42
 
3.2%
Other values (49) 755
57.3%
ValueCountFrequency (%)
21 2
 
0.2%
24 7
 
0.5%
25 6
 
0.5%
26 2
 
0.2%
27 20
1.5%
28 24
1.8%
29 13
 
1.0%
30 23
1.7%
31 38
2.9%
32 30
2.3%
ValueCountFrequency (%)
238 1
 
0.1%
212 1
 
0.1%
96 2
0.2%
93 4
0.3%
84 2
0.2%
79 2
0.2%
78 2
0.2%
77 2
0.2%
76 2
0.2%
74 2
0.2%

cc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct133
Distinct (%)11.8%
Missing189
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean89.72264
Minimum28
Maximum145
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.594501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile70
Q182
median89.5
Q398
95-th percentile109.6
Maximum145
Range117
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.325413
Coefficient of variation (CV)0.13737238
Kurtosis0.86398349
Mean89.72264
Median Absolute Deviation (MAD)7.6
Skewness0.079504367
Sum101296.86
Variance151.9158
MonotonicityNot monotonic
2023-11-20T11:53:47.650674image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 49
 
3.7%
102 40
 
3.0%
90 39
 
3.0%
93 38
 
2.9%
85 36
 
2.7%
84 36
 
2.7%
89 36
 
2.7%
82 32
 
2.4%
96 28
 
2.1%
76 28
 
2.1%
Other values (123) 767
58.2%
(Missing) 189
 
14.3%
ValueCountFrequency (%)
28 1
 
0.1%
52 2
 
0.2%
53 2
 
0.2%
55 2
 
0.2%
57 2
 
0.2%
60 2
 
0.2%
65 2
 
0.2%
66 8
0.6%
67 6
0.5%
68 4
0.3%
ValueCountFrequency (%)
145 1
 
0.1%
142 1
 
0.1%
128 1
 
0.1%
123 2
 
0.2%
121 2
 
0.2%
120 1
 
0.1%
119 4
0.3%
118 3
 
0.2%
117 8
0.6%
116 2
 
0.2%

pp
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)12.6%
Missing1064
Missing (%)80.7%
Infinite0
Infinite (%)0.0%
Mean35.009843
Minimum19
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size10.4 KiB
2023-11-20T11:53:47.701051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile30
Q132
median35
Q337
95-th percentile41
Maximum86
Range67
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.1397545
Coefficient of variation (CV)0.14680884
Kurtosis38.300644
Mean35.009843
Median Absolute Deviation (MAD)3
Skewness3.7936705
Sum8892.5
Variance26.417077
MonotonicityNot monotonic
2023-11-20T11:53:47.746549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
32 31
 
2.4%
35 28
 
2.1%
36 22
 
1.7%
37 20
 
1.5%
33 17
 
1.3%
40 17
 
1.3%
34 17
 
1.3%
31 16
 
1.2%
39 16
 
1.2%
30 14
 
1.1%
Other values (22) 56
 
4.2%
(Missing) 1064
80.7%
ValueCountFrequency (%)
19 2
 
0.2%
24 2
 
0.2%
26 1
 
0.1%
27 1
 
0.1%
27.5 3
 
0.2%
28 1
 
0.1%
29 2
 
0.2%
30 14
1.1%
31 16
1.2%
32 31
2.4%
ValueCountFrequency (%)
86 1
 
0.1%
53 1
 
0.1%
47 1
 
0.1%
43 1
 
0.1%
42 5
 
0.4%
41.15 2
 
0.2%
41 4
 
0.3%
40 17
1.3%
39 16
1.2%
38.5 1
 
0.1%

Interactions

2023-11-20T11:53:45.733498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.370153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.960388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.392847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.840436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.397012image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.870446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.319424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.762019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.295244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.774513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.429132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.005954image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.440469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.889466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.451048image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.916811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.366685image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.805752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.341447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.812737image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.588717image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.046244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.483219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.933299image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.496021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.959302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.410889image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.848509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.384715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.854142image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.645710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.090897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.526548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.981424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.542682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.004884image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.456904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.892346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.429241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.897560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.692749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.135209image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.572184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.027612image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.592285image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.052251image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.503417image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.934500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.475002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.936203image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.739750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.179376image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.617470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.169205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.640041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.102170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.549200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.979086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.519724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.973740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.785526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.223990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.664193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.217492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.690588image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.149366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.594305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.022979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.565177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:46.020770image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.829283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.267379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.708327image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.264977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.737779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.195439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.637065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.066362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.607386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:46.056773image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.871073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.308492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.751969image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.306456image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.779416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.237264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.678387image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.218184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.647265image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:46.097724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:41.919168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.351086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:42.797776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.353894image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:43.830848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.280543image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:44.720381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.258480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-11-20T11:53:45.693528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-11-20T11:53:47.793193image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
epttstdgschdlccppsu
e1.0000.039-0.2290.2880.1190.3360.2890.1170.210-0.2690.1280.105
p0.0391.0000.4550.3700.3430.2520.048-0.3010.7960.5590.3090.134
t-0.2290.4551.0000.0760.097-0.030-0.075-0.1490.1390.1690.6560.105
ts0.2880.3700.0761.0000.7410.2720.110-0.0970.3510.2070.1400.060
td0.1190.3430.0970.7411.0000.1430.128-0.1080.3020.1660.0670.043
gs0.3360.252-0.0300.2720.1431.0000.060-0.1760.2760.0120.1200.076
c0.2890.048-0.0750.1100.1280.0601.0000.2910.1220.0040.1330.109
hdl0.117-0.301-0.149-0.097-0.108-0.1760.2911.000-0.219-0.2740.1900.052
cc0.2100.7960.1390.3510.3020.2760.122-0.2191.0000.3920.1490.135
pp-0.2690.5590.1690.2070.1660.0120.004-0.2740.3921.0000.0940.139
s0.1280.3090.6560.1400.0670.1200.1330.1900.1490.0941.0000.118
u0.1050.1340.1050.0600.0430.0760.1090.0520.1350.1390.1181.000

Missing values

2023-11-20T11:53:46.162030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-20T11:53:46.245364image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-20T11:53:46.337289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

esupttstdgschdlccpp
01811.062.01.55101.070.085.0188.053.081.0NaN
11821.070.01.6793.063.086.0155.054.090.0NaN
21911.056.01.58109.073.093.0143.060.067.033.6
31911.052.21.59108.075.093.0143.060.067.033.6
41911.053.01.54118.072.083.0146.052.073.0NaN
52021.062.01.6990.060.064.0163.037.072.0NaN
62021.061.81.78106.072.099.0146.035.074.031.0
72021.060.51.78109.062.099.0146.035.075.031.0
82021.059.01.71117.074.084.0194.046.078.2NaN
92111.048.11.5799.064.092.0167.039.072.5NaN
esupttstdgschdlccpp
1308611NaN58.01.42118.075.0NaNNaNNaN102.029.0
1309451NaN45.01.50140.070.0NaNNaNNaNNaNNaN
1310831NaN64.01.41120.080.0191.0207.063.0114.033.0
1311651NaN129.01.48160.070.0NaNNaNNaN128.053.0
1312641NaN57.01.44145.080.0NaNNaNNaN88.034.0
1313741NaN54.51.60170.080.089.0130.0212.090.032.0
1314831NaN48.01.55130.070.0NaN169.051.083.027.0
1315472NaN88.01.76130.070.0NaNNaN24.0NaNNaN
1316801NaN54.71.4312.080.098.0175.0NaN95.029.0
1317492NaN48.01.42NaNNaNNaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

esupttstdgschdlccpp# duplicates
282221.062.301.71106.076.076.0149.034.081.0NaN4
5855813.078.401.50160.080.095.0211.043.0109.0NaN4
01811.062.001.55101.070.085.0188.053.081.0NaN2
11821.070.001.6793.063.086.0155.054.090.0NaN2
21911.052.201.59108.075.093.0143.060.067.033.62
31911.053.001.54118.072.083.0146.052.073.0NaN2
41911.056.001.58109.073.093.0143.060.067.033.62
52014.066.651.59105.067.087.0135.036.077.0NaN2
62021.059.001.71117.074.084.0194.046.078.2NaN2
72021.060.501.78109.062.099.0146.035.075.031.02